1. Field of Invention
The present invention is directed to systems and methods for dynamic route estimation and prediction using discrete sampled location updates from various mobile devices, and to also provide supplemental information such as route metrics, including without limitation traveled distance and elapsed time.
2. Description of the Related Art
Computerized mapping software is achieving widespread use today. Such mapping programs are commonly used to automate tasks of calculating routes, viewing location-specific geographical areas for their spatial content, such as addresses, roadways, rivers, etc., and for the purpose of being used with Global Positioning System (GPS) devices for various applications, such as a personal navigation application. Mapping software programs apply to a wide variety of uses, such as personal navigation, telematics, thematic mapping, resource planning, routing, fleet tracking, safety dispatching (i.e., Police, Fire, and Rescue organizations), and a wide variety of specialized Geographic Information System (GIS) applications, all of which are well known to people skilled in the art.
Real-time communication networks today also provide the ability to transfer, in real-time, voice and data information from various mobile devices, such as wireless phones, telemetry devices, or the like, to a multitude of other devices, either mobile or stationary, all of which are well known to people that are skilled in the art. For example, GPS devices that are connected to a wireless MODEM are able to transfer their position coordinates, such as latitude and longitude, wirelessly to a computer or server for later retrieval or real-time viewing of said information. Current applications that integrate or combine mapping, real-time communication capabilities, and position devices, for various computing devices are well known to people skilled in the art. These applications are referred to by various terminologies, including, but not limited to Automatic Vehicle Location (AVL), Location-Based Services (LBS), Fleet Tracking Systems, etc., all of which are well known to people skilled in the art.
Conventional systems, such as AVL systems, typically involve a positioning device connected to a wireless MODEM sending location information, amongst other telemetry information, at discrete time intervals to a computer for the viewing of said information. This monitoring, or tracking, of real-time location information or of location-history information is sometimes referred to as the breadcrumb trail or history information of the mobile device; since it illustrates the current and/or previous locations that the mobile device is or has been in space and time. The problem with the conventional system is that the ‘breadcrumb’ trail does not provide the user with sufficient information about the mobile device's actual or estimated route during the course of its travels, but only provides discrete location information over a specified period of time. How the mobile device traveled along the underling routable network infrastructure, such as roads, highways, exit ramps, etc., from point-to-point is not provided in prior art.
Conventional applications will sometimes associate the term ‘route’ with a breadcrumb trail that directly connects discrete points with straight lines, but this is not an accurate use of the term as known to people that are skilled in the art. For example, a route is typically defined as a road, course, or way for traveling from one place to another over a set of various defined paths, such as a route along a highway. True routing applications include a network of paths that are used in combination with destination points, where destination points can include both an origin and stop points, in order to determine a specific route along said network paths between each of the destination points.
Conventional systems widely use this method of connecting direct lines between location updates for illustrating the breadcrumb trail path and direction between location updated points. Some conventional systems further illustrate the order of the location updates that the mobile device traveled by chronologically numbering each of the location updates or by connecting a direct line from each point, or drawing an arrow at each point, with an arrow illustrating the mobile device's heading or pseudo heading. The problem with the conventional system is that these methods and systems do not provide the user with any actual or estimated route information derived from the location updates, specifically due to the discrete nature of the location data. As people skilled in the art will appreciate, a method and system that can create a dynamic estimated route between various discrete locations would provide a number of improvements over existing prior art, such as providing a better illustration of the data, which has inherent limitations due to its being discrete location data, extrapolating total driving distance from a set of discrete location updates, and providing to the user an ability to save the calculated estimated route or plan new routes from the existing location information.
Thus, a need exits for a method and system that allows an application to dynamically generate estimated route information from location updates originally derived from a mobile positioning device. Until now, an adequate solution to these problems has eluded those skilled in the art. Thus, there exists a need to provide a solution that enables an application to dynamically generate, based on various route generation preferences, estimated and predictive routes using location information that was generated from a mobile positioning device sending discrete location updates of its position over various periods of time. This provides many important benefits for computing devices that receive discrete position updates for the purpose of monitoring, planning, and analysis of mobile devices' positional information.